TCGAbiolinksGUI

1 Instructor names and contact information

Benjamin P. Berman (Benjamin.Berman at cshs.org)
Tiago C. Silva (tiagochst at gmail.com)

2 Workshop description

2.1 Aims

This workshop will:

Introduce you to the TCGA (The Cancer Genome Atlas) data available at the NCI’s Genomic Data Commons (GDC).
Demonstrate how to access and import TCGA data using the R/Bioconductor package TCGAbiolinks (Colaprico et al. 2015)
Provide intruction to visualize copy number alteration and mutation data using the R/Bioconductor package maftools

2.2 Useful links

the NCI’s Genomic Data Commons (GDC): https://gdc.cancer.gov/.
TCGAbiolinks package: http://bioconductor.org/packages/TCGAbiolinks/
maftools package: http://bioconductor.org/packages/maftools/

2.3 Reference articles

TCGAbiolinks:
- Colaprico, Antonio, et al. “TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.” Nucleic acids research 44.8 (2015): e71-e71. Silva, Tiago C., et al. “TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages.” F1000Research 5 (2016). (https://f1000research.com/articles/5-1542/v2)
- Mounir, Mohamed, et al. “New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx.” PLoS computational biology 15.3 (2019): e1006701. (https://doi.org/10.1371/journal.pcbi.1006701)
- Silva TC, Colaprico A, Olsen C et al.TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages [version 2; peer review: 1 approved, 2 approved with reservations]. F1000Research 2016, 5:1542 (https://doi.org/10.12688/f1000research.8923.2).
The NCI’s Genomic Data Commons (GDC):
- Gao, Galen F., et al. “Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data.” Cell systems 9.1 (2019): 24-34. (https://doi.org/10.1016/j.cels.2019.06.006)

2.4 R/Bioconductor packages used

3 Introduction

3.1 Understanding GDC (genomics data common portal)

In this workshop we will access (The Cancer Genome Atlas) data available at the NCI’s Genomic Data Commons (GDC). Before we start, it is important to know that the data is deposit in two different databases: - The legacy archive (https://portal.gdc.cancer.gov/legacy-archive/search/f) which contains unharmonized legacy data from repositories that predate the GDC (e.g. CGHub). Also, legacy data is not actively maintained, processed, or harmonized by the GDC (Source: https://docs.gdc.cancer.gov/Data_Portal/Users_Guide/Legacy_Archive/)

Harmonized database (https://portal.gdc.cancer.gov/) which contains data from many cancer projects processed using standardized pipelines and the reference genome GRCh38 (hg38). This gives the advantage of analyzing multiple cancer types or the same cancer type across multiple projects. You can find more information about the pipelines supporting data harmonization at https://gdc.cancer.gov/about-data/gdc-data-harmonization and at https://docs.gdc.cancer.gov/Encyclopedia/pages/Harmonized_Data/.

An overview of the web portal is available at https://docs.gdc.cancer.gov/Data_Portal/Users_Guide/Getting_Started/.

Figure legend: TCGA databases (blue: legacy archive. red: harmnized database) Source: https://portal.gdc.cancer.gov/

3.2 Understanding TCGA data

3.2.1 Data access

GDC provides the data with two access levels:

Open: includes high level genomic data that is not individually identifiable, as well as most clinical and all biospecimen data elements.
Controlled: includes individually identifiable data such as low level genomic sequencing data, germline variants, SNP6 genotype data, and certain clinical data elements

You can find more information about those two levels and how to get access to controlled data at: https://gdc.cancer.gov/access-data/data-access-processes-and-tools.

3.2.2 TCGA barcode description

Each TCGA sample has a unique identifier called TCGA barcode, which contains important information about each sample. A drescription of the barcode is shown below (Source: https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/).

Figure legend: TCGA barcode Source: https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/

You can find a table with all the code and meanings at https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables. The Sample Type Codes is shown below:

Figure legend: TCGA samples type. Source: https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes

3.2.3 Data structure

In order to filter the data available in GDC some fields are available such as project (TCGA, TARGET, etc), data category (Transcriptome Profiling, DNA methylation,Clinical, etc), data type (Gene Expression Quantification, Isoform Expression Quantification, Methylation Beta Value, etc), experimental strategy (miRNA-Seq, RNA-Seq, etc,), Workflow Type, platfom, access type and others.

Figure legend: Data category filter

Figure legend: Data type filter

In terms of data granularity, a project has data on several categories, each category contains several data types that might have been produced with different workflows,experimental strategy and platforms. In that way, if you select data type “Gene Expression Quantification” the data category will be Transcriptome Profiling.

Figure legend: Filtering by Gene Expression Quantification

You can find the entry possibilities for each filter at the repository page of the database at https://portal.gdc.cancer.gov/repository.

4 Working with TCGA data

During this workshop we will be using open access data from the harmonized GDC database (hg38 version), which can be accessed at https://portal.gdc.cancer.gov/.

Through the next sections we will:

Access TCGA clinical data
Search, download and make an R object from TCGA data

RNA-seq data
DNA methylation

Download samples metadata
Download and visualize mutations
Download and visualize copy number alterations

4.1 Loading required packages

library(TCGAbiolinks)
library(MultiAssayExperiment)
library(maftools)

4.2 Clinical data

clinical <- GDCquery_clinic("TCGA-COAD")

4.3 RNA-Seq data

The RNA-Seq pipeline produces raw counts, FPKM and FPKM-UQ quantifications and is described at https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/.

Figure legend: gene expression pipeline. Souce: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/

Figure legend: mRNA files. Souce: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/

The following options are used to search mRNA results using TCGAbiolinks:

data.category: “Transcriptome Profiling”
data.type: “Gene Expression Quantification”
workflow.type: “HTSeq - Counts”, “HTSeq - FPKM”, “HTSeq - FPKM-UQ”

Here is the example to download the raw counts, which can be used with (http://bioconductor.org/packages/DESeq2/) for differential expression analysis.

query.exp.hg38 <- GDCquery(project = "TCGA-GBM", 
                  data.category = "Transcriptome Profiling", 
                  data.type = "Gene Expression Quantification", 
                  workflow.type = "HTSeq - Counts",
                  barcode =  c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01"))
GDCdownload(query.exp.hg38)
raw.counts <- GDCprepare(query = query.exp.hg38, summarizedExperiment = FALSE)

head(raw.counts)

## # A tibble: 6 x 3
##   X1               `TCGA-14-0736-02A-01R-2005-… `TCGA-06-0211-02A-02R-2005…
##   <chr>                                   <dbl>                       <dbl>
## 1 ENSG00000000003…                         4282                        3853
## 2 ENSG00000000005…                           34                           8
## 3 ENSG00000000419…                          899                        1785
## 4 ENSG00000000457…                          293                         551
## 5 ENSG00000000460…                          143                         378
## 6 ENSG00000000938…                          286                         683

Here is the example to download the FPKM-UQ counts.

query.exp.hg38 <- GDCquery(project = "TCGA-GBM", 
                  data.category = "Transcriptome Profiling", 
                  data.type = "Gene Expression Quantification", 
                  workflow.type = "HTSeq - FPKM-UQ",
                  barcode =  c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01"))
GDCdownload(query.exp.hg38)
fpkm.uq.counts <- GDCprepare(query = query.exp.hg38, summarizedExperiment = FALSE)

head(fpkm.uq.counts)

## # A tibble: 6 x 3
##   X1               `TCGA-14-0736-02A-01R-2005-… `TCGA-06-0211-02A-02R-2005…
##   <chr>                                   <dbl>                       <dbl>
## 1 ENSG00000242268…                        1533.                       3518.
## 2 ENSG00000270112…                        4253.                       4978.
## 3 ENSG00000167578…                      261187.                     135247.
## 4 ENSG00000273842…                           0                           0 
## 5 ENSG00000078237…                      224251.                     180270.
## 6 ENSG00000146083…                      117081.                     197064.

# miRNA
library(TCGAbiolinks)
query.mirna <- GDCquery(project = "TARGET-AML", 
                        experimental.strategy = "miRNA-Seq",
                        data.category = "Transcriptome Profiling", 
                        barcode = c("TARGET-20-PATDNN","TARGET-20-PAPUNR"),
                        data.type = "miRNA Expression Quantification")
GDCdownload(query.mirna)
mirna <- GDCprepare(query = query.mirna,
                     save = TRUE, 
                     save.filename = "mirna.rda")


query.isoform <- GDCquery(project = "TARGET-AML", 
                          experimental.strategy = "miRNA-Seq",
                          data.category = "Transcriptome Profiling", 
                          barcode = c("TARGET-20-PATDNN","TARGET-20-PAPUNR"),
                          data.type = "Isoform Expression Quantification")
GDCdownload(query.isoform)

isoform <- GDCprepare(query = query.isoform,
                    save = TRUE, 
                    save.filename = "mirna-isoform.rda")
DNA methylation: aligned against hg38
#-------------------------------------- 
# DNA methylation data
#-------------------------------------- 
# DNA methylation aligned to hg38
query_met.hg38 <- GDCquery(project= "TCGA-LGG", 
                           data.category = "DNA Methylation", 
                           platform = "Illumina Human Methylation 450", 
                           barcode = c("TCGA-HT-8111-01A-11D-2399-05","TCGA-HT-A5R5-01A-11D-A28N-05"))
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)

4.4 Mutation

TCGAbiolinks has provided a few functions to download mutation data from GDC. There are two options to download the data:

Use GDCquery_Maf which will download MAF aligned against hg38.

This example will download MAF (mutation annotation files) for variant calling pipeline muse. Pipelines options are: muse, varscan2, somaticsniper, mutect. For more information please access GDC docs.

library(maftools)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:matrixStats':
## 
##     count

## The following object is masked from 'package:Biobase':
## 
##     combine

## The following objects are masked from 'package:GenomicRanges':
## 
##     intersect, setdiff, union

## The following object is masked from 'package:GenomeInfoDb':
## 
##     intersect

## The following objects are masked from 'package:IRanges':
## 
##     collapse, desc, intersect, setdiff, slice, union

## The following objects are masked from 'package:S4Vectors':
## 
##     first, intersect, rename, setdiff, setequal, union

## The following objects are masked from 'package:BiocGenerics':
## 
##     combine, intersect, setdiff, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(DT)

maf <- GDCquery_Maf("COAD", pipelines = "muse") %>% read.maf

## ============================================================================

##  For more information about MAF data please read the following GDC manual and web pages:

##  GDC manual: https://gdc-docs.nci.nih.gov/Data/PDF/Data_UG.pdf

##  https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/

##  https://gdc.cancer.gov/about-gdc/variant-calling-gdc

## ============================================================================

## --------------------------------------

## o GDCquery: Searching in GDC database

## --------------------------------------

## Genome of reference: hg38

## --------------------------------------------

## oo Accessing GDC. This might take a while...

## --------------------------------------------

## ooo Project: TCGA-COAD

## --------------------

## oo Filtering results

## --------------------

## ooo By access

## ooo By data.type

## ooo By workflow.type

## ----------------

## oo Checking data

## ----------------

## ooo Check if there are duplicated cases

## ooo Check if there results for the query

## -------------------

## o Preparing output

## -------------------

## Downloading data for project TCGA-COAD

## Of the 1 files for download 1 already exist.

## All samples have been already downloaded

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## -Validating
## -Silent variants: 77997 
## -Summarizing
## --Mutiple centers found
## BCM;WUGSC;WUGSC;BCM;BCM;BI--Possible FLAGS among top ten genes:
##   TTN
##   MUC16
##   SYNE1
##   OBSCN
## -Processing clinical data
## --Missing clinical data
## -Finished in 11.5s elapsed (12.0s cpu)

datatable(getSampleSummary(maf),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

plotmafSummary(maf = maf, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE)

oncoplot(maf = maf, top = 10, removeNonMutated = TRUE)

titv = titv(maf = maf, plot = FALSE, useSyn = TRUE)
#plot titv summary
plotTiTv(res = titv)

4.5 Copy number alteration data

The Copy Number Variation Analysis Pipeline is described at https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/CNV_Pipeline/

Numeric focal-level Copy Number Variation (CNV) values were generated with “Masked Copy Number Segment” files from tumor aliquots using GISTIC2 on a project level. Only protein-coding genes were kept, and their numeric CNV values were further thresholded by a noise cutoff of 0.3:

Genes with focal CNV values smaller than -0.3 are categorized as a “loss” (-1)
Genes with focal CNV values larger than 0.3 are categorized as a “gain” (+1)
Genes with focal CNV values between and including -0.3 and 0.3 are categorized as “neutral” (0).

You can access “Gene Level Copy Number Scores” from GISTIC with the code below:

query <- GDCquery(project = "TCGA-GBM",
             data.category = "Copy Number Variation",
             data.type = "Gene Level Copy Number Scores",              
             access = "open")
GDCdownload(query)
scores <- GDCprepare(query)

scores[1:5,1:5]

## # A tibble: 5 x 5
##   `Gene Symbol`   `Gene ID` Cytoband `TCGA-19-1790-01B-… `TCGA-41-2572-01A…
##   <chr>               <dbl> <chr>                  <dbl>              <dbl>
## 1 ENSG0000000812…         0 1p36.33                    0                  0
## 2 ENSG0000000813…         0 1p36.33                    0                  0
## 3 ENSG0000006760…         0 1p36.33                    0                  0
## 4 ENSG0000007836…         0 1p36.33                    0                  0
## 5 ENSG0000007880…         0 1p36.33                    0                  0

You can visualize the data using the R/Bioconductor package complexHeatmap.

library(ComplexHeatmap)
library(dplyr)

scores.matrix <- scores[,-c(1:3)] %>% as.matrix
rownames(scores.matrix) <- paste0(scores$`Gene Symbol`,"_",scores$Cytoband)

# gain in more than 100 samples
gain.more.than.hundred.samples <- which(rowSums(scores.matrix == 1) > 100)

# loss in more than 100 samples
loss.more.than.hundred.samples <- which(rowSums(scores.matrix == -1) > 100)

Heatmap(scores.matrix[c(gain.more.than.hundred.samples,loss.more.than.hundred.samples),],
        show_column_names = FALSE, 
        show_row_names = TRUE,
         row_names_gp = gpar(fontsize = 8),
        col = circlize::colorRamp2(c(-1,0,1), colors = c("red","white","blue")))

4.6 DNA methylation data

4.7 ATAC-Seq data

Please, check our ATAC-seq Workshop: http://rpubs.com/tiagochst/atac_seq_workshop

5 Session information

sessionInfo()

## R version 3.5.3 Patched (2019-03-11 r76979)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] ComplexHeatmap_2.1.0        DT_0.5                     
##  [3] dplyr_0.8.3                 maftools_2.0.16            
##  [5] MultiAssayExperiment_1.8.3  SummarizedExperiment_1.12.0
##  [7] DelayedArray_0.8.0          BiocParallel_1.16.6        
##  [9] matrixStats_0.54.0          Biobase_2.42.0             
## [11] GenomicRanges_1.34.0        GenomeInfoDb_1.18.2        
## [13] IRanges_2.16.0              S4Vectors_0.20.1           
## [15] BiocGenerics_0.28.0         TCGAbiolinks_2.13.4        
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.4             circlize_0.4.6             
##   [3] aroma.light_3.12.0          NMF_0.21.0                 
##   [5] plyr_1.8.4                  selectr_0.4-1              
##   [7] ConsensusClusterPlus_1.46.0 lazyeval_0.2.2             
##   [9] splines_3.5.3               crosstalk_1.0.0            
##  [11] ggplot2_3.2.1               gridBase_0.4-7             
##  [13] sva_3.30.1                  digest_0.6.20              
##  [15] foreach_1.4.7               htmltools_0.3.6            
##  [17] fansi_0.4.0                 magrittr_1.5               
##  [19] memoise_1.1.0               cluster_2.0.7-1            
##  [21] doParallel_1.0.15           limma_3.38.3               
##  [23] Biostrings_2.50.2           readr_1.3.1                
##  [25] annotate_1.60.1             wordcloud_2.6              
##  [27] R.utils_2.9.0               prettyunits_1.0.2          
##  [29] colorspace_1.4-1            blob_1.2.0                 
##  [31] rvest_0.3.4                 ggrepel_0.8.1              
##  [33] xfun_0.8                    crayon_1.3.4               
##  [35] RCurl_1.95-4.12             jsonlite_1.6               
##  [37] genefilter_1.64.0           zeallot_0.1.0              
##  [39] survival_2.43-3             zoo_1.8-6                  
##  [41] iterators_1.0.12            glue_1.3.1                 
##  [43] survminer_0.4.5             registry_0.5-1             
##  [45] gtable_0.3.0                zlibbioc_1.28.0            
##  [47] XVector_0.22.0              GetoptLong_0.1.7           
##  [49] shape_1.4.4                 scales_1.0.0               
##  [51] DESeq_1.34.1                rngtools_1.3.1             
##  [53] DBI_1.0.0                   edgeR_3.24.3               
##  [55] bibtex_0.4.2                ggthemes_4.2.0             
##  [57] Rcpp_1.0.2                  xtable_1.8-4               
##  [59] progress_1.2.2              clue_0.3-57                
##  [61] bit_1.1-14                  matlab_1.0.2               
##  [63] km.ci_0.5-2                 htmlwidgets_1.3            
##  [65] httr_1.4.1                  RColorBrewer_1.1-2         
##  [67] pkgconfig_2.0.2             XML_3.98-1.20              
##  [69] R.methodsS3_1.7.1           utf8_1.1.4                 
##  [71] locfit_1.5-9.1              later_0.8.0                
##  [73] reshape2_1.4.3              tidyselect_0.2.5           
##  [75] rlang_0.4.0                 AnnotationDbi_1.44.0       
##  [77] munsell_0.5.0               tools_3.5.3                
##  [79] cli_1.1.0                   downloader_0.4             
##  [81] generics_0.0.2              RSQLite_2.1.2              
##  [83] broom_0.5.2                 evaluate_0.14              
##  [85] stringr_1.4.0               yaml_2.2.0                 
##  [87] knitr_1.24                  bit64_0.9-7                
##  [89] survMisc_0.5.5              purrr_0.3.2                
##  [91] EDASeq_2.16.3               nlme_3.1-137               
##  [93] mime_0.7                    R.oo_1.22.0                
##  [95] xml2_1.2.2                  biomaRt_2.38.0             
##  [97] compiler_3.5.3              curl_4.0                   
##  [99] png_0.1-7                   ggsignif_0.6.0             
## [101] tibble_2.1.3                geneplotter_1.60.0         
## [103] stringi_1.4.3               GenomicFeatures_1.34.6     
## [105] lattice_0.20-38             Matrix_1.2-15              
## [107] KMsurv_0.1-5                vctrs_0.2.0                
## [109] pillar_1.4.2                GlobalOptions_0.1.0        
## [111] data.table_1.12.2           bitops_1.0-6               
## [113] httpuv_1.5.1                rtracklayer_1.42.2         
## [115] R6_2.4.0                    latticeExtra_0.6-28        
## [117] hwriter_1.3.2               promises_1.0.1             
## [119] ShortRead_1.40.0            gridExtra_2.3              
## [121] codetools_0.2-16            assertthat_0.2.1           
## [123] pkgmaker_0.27               rjson_0.2.20               
## [125] withr_2.1.2                 GenomicAlignments_1.18.1   
## [127] Rsamtools_1.34.1            GenomeInfoDbData_1.2.0     
## [129] mgcv_1.8-27                 hms_0.5.0                  
## [131] prettydoc_0.3.0             tidyr_0.8.3                
## [133] rmarkdown_1.14              ggpubr_0.2.2               
## [135] shiny_1.3.2

6 Workshop materials

6.1 Workshops HTMLs

ELMER data Workshop HTML: http://rpubs.com/tiagochst/elmer-data-workshop-2019
ELMER analysis Workshop HTML: http://rpubs.com/tiagochst/ELMER_workshop
ATAC-seq Workshop HTML: http://rpubs.com/tiagochst/atac_seq_workshop

6.2 Workshop videos

We have a set of recorded videos, explaining some of the workshops.

All videos playlist: https://www.youtube.com/playlist?list=PLoDzAKMJh15kNpCSIxpSuZgksZbJNfmMt
ELMER algorithm: https://youtu.be/PzC31K9vfu0
ELMER data: https://youtu.be/R00wG--tGo8
ELMER analysis part1 : https://youtu.be/bcd4uyxrZCw
ELMER analysis part2: https://youtu.be/vcJ_DSCt4Mo
ELMER summarizing several analysis: https://youtu.be/moLeik7JjLk
ATAC-Seq workshop: https://youtu.be/3ftZecz0lU4

References

Colaprico, Antonio, Tiago C Silva, Catharina Olsen, Luciano Garofano, Claudia Cava, Davide Garolini, Thais S Sabedot, et al. 2015. “TCGAbiolinks: An R/Bioconductor Package for Integrative Analysis of Tcga Data.” Nucleic Acids Research 44 (8): e71–e71.

Workshop - TCGA data analysis

Benjamin P. Berman, Tiago C. Silva

2019-08-29